'''simple docstring'''




class        a__ :






     def __init__(					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    0
         __lowerCamelCase            =    0
         __lowerCamelCase            =    {}






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]    ,							a						:						str       ):





         """simple docstring"""



         if vertex not in self.adjacency:
             __lowerCamelCase            =    {}
             self.num_vertices += 1






     def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						str    ,							a						:						Tuple    ,							a						:						int       ):





         """simple docstring"""



         self.add_vertex(a       )
         self.add_vertex(a       )

         if head == tail:
             return

         __lowerCamelCase            =    weight
         __lowerCamelCase            =    weight






     def   SCREAMING_SNAKE_CASE__  (					self						:						Optional[int]       ):





         """simple docstring"""



         __lowerCamelCase            =    self.get_edges()
         for edge in edges:
             __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    edge
             edges.remove((tail, head, weight)       )
         for i in range(len(a       )       ):
             __lowerCamelCase            =    list(edges[i]       )

         edges.sort(key=lambda a       : e[2]       )
         for i in range(len(a       ) - 1       ):
             if edges[i][2] >= edges[i + 1][2]:
                 __lowerCamelCase            =    edges[i][2] + 1
         for edge in edges:
             __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    edge
             __lowerCamelCase            =    weight
             __lowerCamelCase            =    weight






     def __str__(					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    ''''''
         for tail in self.adjacency:
             for head in self.adjacency[tail]:
                 __lowerCamelCase            =    self.adjacency[head][tail]
                 string += f"""{head} -> {tail} == {weight}\n"""
         return string.rstrip('''\n'''       )






     def   SCREAMING_SNAKE_CASE__  (					self						:						Union[str, Any]       ):





         """simple docstring"""



         __lowerCamelCase            =    []
         for tail in self.adjacency:
             for head in self.adjacency[tail]:
                 output.append((tail, head, self.adjacency[head][tail])       )
         return output






     def   SCREAMING_SNAKE_CASE__  (					self						:						int       ):





         """simple docstring"""



         return self.adjacency.keys()






     @staticmethod
     def   SCREAMING_SNAKE_CASE__  (					a						:						Optional[Any]=None    ,							a						:						Tuple=None       ):





         """simple docstring"""



         __lowerCamelCase            =    Graph()
         if vertices is None:
             __lowerCamelCase            =    []
         if edges is None:
             __lowerCamelCase            =    []
         for vertex in vertices:
             g.add_vertex(a       )
         for edge in edges:
             g.add_edge(*a       )
         return g




     class        a__ :






          def __init__(					self						:						Union[str, Any]       ):





              """simple docstring"""



              __lowerCamelCase            =    {}
              __lowerCamelCase            =    {}






          def __len__(					self						:						List[str]       ):





              """simple docstring"""



              return len(self.parent       )






          def   SCREAMING_SNAKE_CASE__  (					self						:						Any    ,							a						:						int       ):





              """simple docstring"""



              if item in self.parent:
                  return self.find(a       )

              __lowerCamelCase            =    item
              __lowerCamelCase            =    0
              return item






          def   SCREAMING_SNAKE_CASE__  (					self						:						List[str]    ,							a						:						str       ):





              """simple docstring"""



              if item not in self.parent:
                  return self.make_set(a       )
              if item != self.parent[item]:
                  __lowerCamelCase            =    self.find(self.parent[item]       )
              return self.parent[item]






          def   SCREAMING_SNAKE_CASE__  (					self						:						str    ,							a						:						Optional[int]    ,							a						:						List[str]       ):





              """simple docstring"""



              __lowerCamelCase            =    self.find(a       )
              __lowerCamelCase            =    self.find(a       )

              if roota == roota:
                  return roota

              if self.rank[roota] > self.rank[roota]:
                  __lowerCamelCase            =    roota
                  return roota

              if self.rank[roota] < self.rank[roota]:
                  __lowerCamelCase            =    roota
                  return roota

              if self.rank[roota] == self.rank[roota]:
                  self.rank[roota] += 1
                  __lowerCamelCase            =    roota
                  return roota
              return None






     @staticmethod
     def   SCREAMING_SNAKE_CASE__  (					a						:						Any       ):





         """simple docstring"""



         __lowerCamelCase            =    graph.num_vertices

         __lowerCamelCase            =    Graph.UnionFind()
         __lowerCamelCase            =    []
         while num_components > 1:
             __lowerCamelCase            =    {}
             for vertex in graph.get_vertices():
                 __lowerCamelCase            =    -1

             __lowerCamelCase            =    graph.get_edges()
             for edge in edges:
                 __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    edge
                 edges.remove((tail, head, weight)       )
             for edge in edges:
                 __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    edge
                 __lowerCamelCase            =    union_find.find(a       )
                 __lowerCamelCase            =    union_find.find(a       )
                 if seta != seta:
                     if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                         __lowerCamelCase            =    [head, tail, weight]

                     if cheap_edge[seta] == -1 or cheap_edge[seta][2] > weight:
                         __lowerCamelCase            =    [head, tail, weight]
             for vertex in cheap_edge:
                 if cheap_edge[vertex] != -1:
                     __lowerCamelCase      ,    __lowerCamelCase      ,    __lowerCamelCase            =    cheap_edge[vertex]
                     if union_find.find(a       ) != union_find.find(a       ):
                         union_find.union(a    ,							a       )
                         mst_edges.append(cheap_edge[vertex]       )
                         __lowerCamelCase            =    num_components - 1
         __lowerCamelCase            =    Graph.build(edges=a       )
         return mst

'''simple docstring'''




import argparse

import torch

from transformers import (
    UniSpeechSatConfig,
    UniSpeechSatForAudioFrameClassification,
    UniSpeechSatForSequenceClassification,
    UniSpeechSatForXVector,
    WavaVecaFeatureExtractor,
    logging,
)


logging.set_verbosity_info()
__UpperCAmelCase					=logging.get_logger(__name__)






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    UniSpeechSatForSequenceClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''projector.weight''']
    __lowerCamelCase            =    downstream_dict['''projector.bias''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.post_net.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Union[str, Any]:
    __lowerCamelCase            =    UniSpeechSatForAudioFrameClassification.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''model.linear.weight''']
    __lowerCamelCase            =    downstream_dict['''model.linear.bias''']
    return model






def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       List[Any]:
    __lowerCamelCase            =    UniSpeechSatForXVector.from_pretrained(UpperCamelCase__							,    config=UpperCamelCase__							)
    __lowerCamelCase            =    downstream_dict['''connector.weight''']
    __lowerCamelCase            =    downstream_dict['''connector.bias''']
    for i, kernel_size in enumerate(hf_config.tdnn_kernel							):
        __lowerCamelCase            =    downstream_dict[
            f"""model.framelevel_feature_extractor.module.{i}.kernel.weight"""
        ]
        __lowerCamelCase            =    downstream_dict[f"""model.framelevel_feature_extractor.module.{i}.kernel.bias"""]

    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear1.bias''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.weight''']
    __lowerCamelCase            =    downstream_dict['''model.utterancelevel_feature_extractor.linear2.bias''']
    __lowerCamelCase            =    downstream_dict['''objective.W''']
    return model






@torch.no_grad()
def __lowerCAmelCase					(				UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)      ->       Optional[Any]:
    __lowerCamelCase            =    torch.load(UpperCamelCase__							,    map_location='''cpu'''							)

    __lowerCamelCase            =    checkpoint['''Downstream''']

    __lowerCamelCase            =    UniSpeechSatConfig.from_pretrained(UpperCamelCase__							)
    __lowerCamelCase            =    WavaVecaFeatureExtractor.from_pretrained(
        UpperCamelCase__							,    return_attention_mask=UpperCamelCase__							,    do_normalize=UpperCamelCase__							)

    __lowerCamelCase            =    hf_config.architectures[0]
    if arch.endswith('''ForSequenceClassification'''							):
        __lowerCamelCase            =    convert_classification(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForAudioFrameClassification'''							):
        __lowerCamelCase            =    convert_diarization(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    elif arch.endswith('''ForXVector'''							):
        __lowerCamelCase            =    convert_xvector(UpperCamelCase__							,    UpperCamelCase__							,    UpperCamelCase__							)
    else:
        raise NotImplementedError(f"""S3PRL weights conversion is not supported for {arch}"""							)

    if hf_config.use_weighted_layer_sum:
        __lowerCamelCase            =    checkpoint['''Featurizer''']['''weights''']

    hf_feature_extractor.save_pretrained(UpperCamelCase__							)
    hf_model.save_pretrained(UpperCamelCase__							)


if __name__ == "__main__":
  __UpperCAmelCase					=argparse.ArgumentParser()
  parser.add_argument(
      "--base_model_name", default=None, type=str, help="Name of the huggingface pretrained base model."
  )
  parser.add_argument("--config_path", default=None, type=str, help="Path to the huggingface classifier config.")
  parser.add_argument("--checkpoint_path", default=None, type=str, help="Path to the s3prl checkpoint.")
  parser.add_argument("--model_dump_path", default=None, type=str, help="Path to the final converted model.")
  __UpperCAmelCase					=parser.parse_args()
  convert_saprl_checkpoint(args.base_model_name, args.config_path, args.checkpoint_path, args.model_dump_path)
